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Tiansi Dong Department of Computer Science University of Hagen Germany. Modeling Human Intelligence as a Slow Intelligence System. Outline. Slow Intelligence System (SIS) Properties of Human Intelligence The question Case study in Spatial Reasoning Within one snapshot view - PowerPoint PPT Presentation
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Modeling Human Intelligence as a Slow Intelligence System
Tiansi Dong
Department of Computer ScienceUniversity of Hagen
Germany
Outline
Slow Intelligence System (SIS) Properties of Human Intelligence The question Case study in Spatial Reasoning
– Within one snapshot view
– Between snapshot views Conclusion Outlooks
Slow Intelligence System
Solve problems by tryingContext-awareMay not perform well in a short runLearn to improve its performance
Slow Intelligence System
Problem enumerator adaptor concentratoreliminator Solution
propagator
timing controller
environment
environment
Human Intelligence is_a Slow Intelligence
Slow developmental
infants
pupils
students
doctors
professors
Properties of Human Intelligence
Now suppose it not about apple, rather
football moneybus
Spatial cognition is foundamental
Question Human intelligence is_a Slow Intelligence
System Spatial intelligence is foundamental to human
intelligence Slow Intelligence System has_a architechture Is it possible that spatial intelligence be
simulated within the SIS architechture?
A picture on a wall A lady in the picture The lady is back to
us A gentalman is near
the picture The gentalman is at
the left side of the picture
SIS for Spatial Knowledge within a Scene
Object categories
– A picture
– A lady
– A wall Spatial relations
– On, in
– Back, left
– Near
SIS for Spatial Knowledge within a Scene
Object categories
– A picture
– A lady
– A wall
Specific Question
Cross linguistic spatial relations
– in, on, near, front, left,...
– 上,左,前
?
Results in Psychology
Connection relation is primitive Orientation and distance relations are acquired
– Piaget (1954) The Construction of Reality in the Child. Routledge & Kegan Paul Ltd.
– Carey (2009) The Origin of Concepts. Oxford Press
Some existing work Neural Network
– Terry Regier (1996) “The Human Semantic Potential”, MIT Press.
• Spatial model is point-based
• 'connection' is not primitive Formal logic
– De Laguna (1922) Point, line and surface as sets of solids, The Journal of Philosophy
– T Dong (2008) Comment on RCC–From RCC to RCC++, Journal of Philosophical Logic
• Spatial model is region-based
• 'connection' is primitive
Object categories
Case study in Spatial Reasoning in SIS
+ Connection relation
near, in, on, left, right, ...
Context-aware
Problem-solving by trying
Spatial Reasoning for 'one foot away'
B
A
Trying all possible extension (problem solving by trying), and see whether one foot connects with the target object (context awareness).
∃foot [foot ∈ FOOT ⋀ C(A, foot) ⋀ C(foot, B)]
Spatial Reasoning for distance in SIS
In the UK “A is one foot away from B” means region B can be reached by a region of the same size as the British imperial foot from A.
China and Egypt Cun: the body segment between the wrist striation behind the thumb and
the pulsing point of the radial artery; Cubit: the segment between the bent elbow and the point of extended
middle finger. In modern physics: meter, light-year.
The meter is the distance traveled by light in vacuum during a time interval of 1/299 792 458 of a second
A B
X Y
Spatial Reasoning for distance comparison
“A is nearer to B than to C”: there is an X such that C(A, X) ⋀ C(X, B) and there is no X such that C(A, X) ⋀ C(X, B).
B
C
A
XX
Trying all possible extension (problem solving by trying), and see whether one x connects with B and non of x connects with C (context awareness).
x
Spatial Reasoning for orientation
“A is in front of B”: A is nearer to the front part of B than to its other parts.
BB
A
Spatial Reasoning for orientation
Orientation is determined by the shape of the reference object, and the method of distance comparison.
ReferenceObject
NW
SW
W
E
N NE
SES
Spatial Reasoning Performance
Performance in term of the accuracy increases, as the number of sides of the reference object increases.
Qualitative spatial orientation frameworks, e.g. Frank (1992), Freksa (1992), Hernández (1994), Freksa (1999)Renz and Mitra (2004), Dong and Guesgen (2007)
Quantitative spatial orientation frameworks,Euclidean geometry
Spatial Reasoning Performance
P: ae-iθ
Q: e-iθ
θ
The orientation of P can be defined as the point on the unit circle which is nearest to P.
O 1
W
SIS for Spatial Reasoning for one scene: Short Summary
Context-aware (Object, Connection)
Always trying (do spatial extension)
Continuously improve performance(do adaptation)
SIS for Spatial Reasoning between scene:Object tracing
Fast changining leads to an illusion
bird → rabbit, rabbit → bird
Otherwise, bird flies, rabbit moves
SIS for Spatial Reasoning between scene:Object tracing
A problem of object mapping between scenes Two object tracing results due to two different
priorities
– Priority on spatial changes (minimal spatial changes)
– Priority on object categories (objects are mapped within same categories)
SIS1 for Object tracing with priority on spatial changes
[permutation] list all possible mappings
[elimination+concentration] choose the mapping
with the minimal spatial changes
SIS2 for Object tracing with priority on object category
[permutation] list all possible mappings [elimination] remove mappings of different object
categories
[elimination+concentration] choose the mapping within minimal spatial changes
Why fast changing leads to illustiion?
Conjection: SIS2 takes more time than SIS1 in object mapping between scenes.
Conclusion
SIS shall be a Cognitive Architecture
– SIS for spatial knowldge acquisition within a scene
– SIS for spatial knowledge acquisition between scenes
– SIS for Spatial Cognition
– Spatial Cognition is foundamental to Human Intelligence
– SIS as a Cognitive Architecture for Human Intelligence